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- W1507884788 abstract "How to obtain maximum generalization and fault-tolerance has been an important issue in designing a feedforward network. Research on rule-based neural networks suggests that generalization of a neural network is related to the directions of the pattern vectors encoded by hidden units, while fault-tolerance depends on the magnitudes of the weights. In this paper, a rule-based neural network is shown better than a standard neural network both in generalization and fault tolerance. In addition, a formal measure for evaluating network fault tolerance is introduced. >" @default.
- W1507884788 created "2016-06-24" @default.
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- W1507884788 date "2002-12-17" @default.
- W1507884788 modified "2023-09-25" @default.
- W1507884788 title "Generalization and fault tolerance in rule-based neural networks" @default.
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- W1507884788 doi "https://doi.org/10.1109/icnn.1994.374386" @default.
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